Efficiently Mining Recurrent Substructures from Protein Three-Dimensional Structure Graphs

Abstract

Studying protein structures is a major asset for understanding the molecular mechanisms of life. The number of publicly available protein structures has increasingly become extremely large. Yet, the classification of a protein structure remains a difficult, costly, and time-consuming task. Exploring spatial information on protein structures can provide important functional and structural insights. In this context, spatial motifs may correspond to relevant fragments, which might be very useful for a better understanding of proteins. In this article, we propose AntMot, a fast algorithm, to find spatial motifs from protein three-dimensional structures by extending the Karp-Miller-Rosenberg repetition finder, originally dedicated to sequences. The extracted motifs, termed ant-motifs, follow an ant-like shape that is composed of a backbone fragment from the primary structure, enriched with spatial refinements. We show that these motifs are biologically sound, and we used them as descriptors in the classification of several benchmark datasets. Experimental results show that our approach presents a trade-off between sequential motifs and subgraph motifs in terms of the number of extracted substructures, while providing a significant enhancement in the classification accuracy over sequential and frequent-subgraph motifs as well as alignment-based approaches.

Mining frequent approximate patterns in large networks

“…Besides, graphs are a powerful modeling tool to represent the network's information structurally. In this context, the techniques we developed for the MFP algorithm are general, which can be applied to graphs from other domains, e.g., social networks [33], satellite images analysis [34], web analysis, etc. We present a new algorithm named MFP, which identifies frequent patterns in large graphs according to the input frequency threshold.…”

“…[8][9][10][11][12] Frequent patterns can be mined from large graphs, either oriented or nonoriented, that model a specific type of a network (social, medical, vegetation, etc.). [13][14][15][16] We note that a graph is a collection of nodes and edges with labels or attributes. An essential challenge for complex graphs [17][18][19] is the detection of frequent patterns.…”

“…In the literature, several works have attempted to identify frequent patterns in a large graph through the use of deep first search (DFS) mechanisms and have focused on identifying recurring patterns following a pattern growth strategy. 15,24,26 In recent works, frequent approximate pattern mining is proposed as an extension of existing FPM algorithms. 15,21,24,[27][28][29][30][31][32] A major challenge is to find approximate occurrences of patterns in single oriented and large graphs.…”

“…15,24,26 In recent works, frequent approximate pattern mining is proposed as an extension of existing FPM algorithms. 15,21,24,[27][28][29][30][31][32] A major challenge is to find approximate occurrences of patterns in single oriented and large graphs. 12 Finding patterns might be easy for graph isomorphism, but not for mining approximate patterns in a large graph.…”

Mining peptides against SARS-CoV-2 entry from constructed RBMs-hACE2 isomer libraries using machine learning and molecular docking

“…Initially, an X-ray crystal structure data of SARS-CoV-2 RBD / hACE2 complex was retrieved from RCSB Protein Data Bank (PDB, https://www.rcsb.org /structure/6MOJ) 8 . Three segments of RBMs-hACE2, Q24-Q42 / F72-Q86 / T347-M360, were determined and selected as primary templates for the peptide design according to a literature search and the definition of sequential and spatial motifs 8,52 (Figure 1-3). All sequential residues from three original RBMs-hACE2 templates were denoted as wild-type segments in this study.…”